Counterfactual explanations help users understand why machine learned models make certain decisions, and more specifically, how these decisions can be changed. In this work, we frame the problem of finding counterfactual explanations -- the minimal perturbation to an input such that the prediction changes -- as an optimization task. Previously, optimization techniques for generating counterfactual examples could only be applied to differentiable models, or alternatively via query access to the model by estimating gradients from randomly sampled perturbations. In order to accommodate non-differentiable models such as tree ensembles, we propose using probabilistic model approximations in the optimization framework. We introduce a novel approximation technique that is effective for finding counterfactual explanations while also closely approximating the original model. Our results show that our method is able to produce counterfactual examples that are closer to the original instance in terms of Euclidean, Cosine, and Manhattan distance compared to other methods specifically designed for tree ensembles.

为了解决机器学习模型的可解释性问题，本研究提出了一种基于梯度优化和概率模型逼近的反事实解释方法，可以适用于不可微模型如树模型，并且该方法得出的反事实案例要优于其他针对树模型的反事实方法。

基于 Tree 集成的灵活可优化反事实解释解释（FOCUS）